Perceptual decision making is one of the most basic forms of cognition, for it is how sensory input is mapped to specific behavior. Substantial effort has focused on uncovering the constituent cortical networks involved in this early form of cognition using both single and multi-unit recordings in primates and, more recently, functional neuroimaging in humans. These neuroimaging studies, typically utilizing functional magnetic resonance imaging (fMRI), have identified areas in frontal, parietal, and thalamic cortex in which metabolic activity correlates with decision related variables. However, decision making is a dynamic process, and the localized activations found with fMRI must be a part of cortical networks defined by the relative timing of these activations and their causality. The overall goal of this project is to couple high temporal resolution, single-trial analysis of electroencephalography (EEG) with simultaneously acquired fMRI to infer the constituent cortical networks of perceptual decision making in the human brain. Specific aims are 1) to replicate and systematically expand upon our prior results showing neural components correlate with task-relevant decision making variables, but for the case of EEG acquired simultaneously with fMRI, 2) to link trial-to-trial variability of EEG components, identified for perceptual decision making, with spatial area simultaneously imaged with fMRI, and 3) to extend our perceptual decision making paradigm from brief stimulus presentation to prolonged and dynamic stimuli and use single-trial analysis of simultaneous EEG/fMRI to differentiate cortical networks involved in evidence accumulation. This project will significantly advance our understanding of decision making in the human brain by providing a more precise cortical network diagram which could be used to better compare differences observed between primate and human data. Finally, this research could lead to a better understanding of cortical processing underlying basic cognitive deficits, linking spatial and temporal changes in activations to specific neurological diseases and disease states.